editor@jaibdd.com
+91 9866031454
e-ISSN: 3049-2122
logo

Journal of Artificial Intelligence and Big Data Disciplines

LoginTrack
Published

Agentic AI Architectures for Explainable GRC in Distributed Data Center Environments

Dhanaraj Sathiri

Published in Volume 3 Issue 1 (January-March 2026) (Vol. 3, Issue 1, 2026)

Agentic AI Architectures for Explainable GRC in Distributed Data Center Environments - Issue cover

Keywords

Enterprise GRCRisk ManagementCorporate GovernanceSecurity OperationsAutonomous AIExplainable AIAgent-Based ModellingData Center SecurityGlobal Data ProtectionRegulatory Compliance.

Abstract

The Future of Enterprise Governance, Risk Management, and Compliance (GRC) will be supported by Autonomous, Explainable, and Agentic Artificial Intelligence (AI) research efforts within Security and Safety Hubs. The need for continuous Security and Compliance Monitoring and Incident Response Management is urgent in Global and Multinational Enterprises – including the related partners, vendors, and suppliers in Supply Chains. These Operations need to keep up with technological evolution, Security Threats and Exploits, Fugitive Software, and Vulnerability exploitations. AI can play a major role in providing always-on 24/7/365 coverage at all layers of Security Architectures – IT, IIOT, OT, Cloud Services, OT, and Physical Security. The implementation of Ground Rules, Frameworks, and Ethical considerations. for responsible AI usage in enterprises will enable rapid deployment without a major oversight component. Emerging trends in Enterprise GRC are actively addressing the following three key requirements: The decision making and operation of more advanced AI components including agent models, AI OS and AI Safety needing to be trustworthy, reliable and safe; automating possible unsafe, unwarranted and undesired behavior in these components; and enabling global, regional and country-local GRC AI coverage. The first requirement is mainly targeted by Advances in Explainable AI and in Safety Mechanisms and Control Architectures, the second requirement by the assessment of various facets of Autonomy – and the third requirement by the application domain of Data Centers.

References

  1. [1][1]Papagiannidis, E. (2025). Responsible artificial intelligence governance: A review of principles and practices. Information Processing & Management, 62(2), 103–118.
  2. [2][2]Yeo, W. J., & Lee, S. (2025). A comprehensive review on financial explainable artificial intelligence. Artificial Intelligence Review, 58(4), 1–35.
  3. [3][3]Yildiz, K., & Karakaya, G. (2025). A systematic literature review on applications of explainable artificial intelligence in financial services. Journal of Finance and Data Science, 11(1), 45–67.
  4. [4][4]Choowan, P., & Lim, C. (2025). Artificial intelligence in data governance for financial decision-making: A systematic analysis. Informatics, 10(1), 8.
  5. [5][5]Verma, H., & Singh, R. (2025). Can AI be auditable? Frameworks for governance, compliance, and lifecycle assurance. arXiv preprint arXiv:2509.00575.
  6. [6][6]Desai, H., & Patel, K. (2024). Explainable AI models for financial regulatory audits. SSRN Electronic Journal.
  7. [7][7]Staley, I. (2025). The role of explainable AI in enhancing trust and decision-making in financial services. Journal of Applied Finance & Banking, 15(5), 1–13.
  8. [8][8]Chung, N. C., Chung, H., Lee, H., Brocki, L., Chung, H., & Dyer, G. (2024). False sense of security in explainable artificial intelligence (XAI). arXiv preprint arXiv:2405.03820.
  9. [9][9]Batool, A., Zowghi, D., & Bano, M. (2023). Responsible AI governance: A systematic literature review. arXiv preprint arXiv:2401.10896.
  10. [10][10]Pi, Y. (2023). Algorithmic governance for explainability: A comparative overview of progress and trends. arXiv preprint arXiv:2303.00651.
  11. [11][11]Ponick, E., & Wieczorek, G. (2022). Artificial intelligence in governance, risk and compliance: Applications and potentials. arXiv preprint arXiv:2212.03601.
  12. [12][12]Bank for International Settlements. (2024). Supervisory insights on explainability and AI governance in financial systems.
  13. [13][13]National Institute of Standards and Technology. (2023). AI risk management framework (AI RMF 1.0).
  14. [14][14]European Central Bank. (2024). Artificial intelligence in financial stability: Benefits and risks. Financial Stability Review.
  15. [15][15]CFA Institute. (2024). Data governance and AI risk management in financial services. CFA Institute Research Reports.
  16. [16][16]Sharma, P., & Gupta, R. (2025). Explainable AI for regulatory compliance: Balancing accuracy and interpretability. International Journal of AI and Data Analytics, 7(2), 45–59.
  17. [17][17]Adeyemi, T., & Okafor, L. (2025). The role of explainable AI in promoting transparency in financial compliance systems. World Journal of Advanced Research and Reviews, 18(2), 112–128.
  18. [18][18]Kumar, S., & Rao, V. (2024). Exploring the role of explainable AI in compliance models for cybersecurity and finance. International Journal of Latest Technology in Engineering, Management & Applied Science, 13(4), 55–63.
  19. [19][19]Montreal AI Ethics Institute. (2024). The importance of audit in AI governance: Ensuring transparency and compliance.
  20. [20][20]IBM Institute for Business Value. (2024). The enterprise guide to AI governance: Building explainable and auditable systems.

Authors (1)

Dhanaraj Sathiri

JNTU

View all publications →

Download Article

PDF

Best for printing and citation

File size: 0.9 MB
Format: PDF

Download Article

PDF

Best for printing and citation

File size: 0.9 MB
Format: PDF

Article Information

Published in:

Volume 3 Issue 1 (January-March 2026) (Vol. 3, Issue 1, 2026)

jaibdd230010

jaibdd-01-000075

2026-03-07

Article Impact

Views:1,987
Downloads:1,011

How to Cite

Sathiri (2026). Agentic AI Architectures for Explainable GRC in Distributed Data Center Environments. Journal of Artificial Intelligence and Big Data Disciplines, 3(1), xx-xx. https://jaibdd.com/articles/jaibdd230010

Article Actions

Whatsapp